Utility mining is gaining attention towards researchers, as it discovers semantic significance among the items in a database. Utility association rule mining is one of utility mining techniques that retrieves highly profitable and highly associated products in a database. Many researchers started to replace traditional association rule mining with utility association rule mining, since utility association rules can reflect both association and semantic significance among the products retrieved from the database. Utility based association rule mining can be applied on various domains like Bio-informatics, Recommender systems, Medical database, Web mining, Image mining. This research work aims to provide in depth study on utility based association rule mining. The work also illustrates the need for utility association rules, by providing drawbacks of traditional association rules. The work also lists existing utility association rules algorithms.

Utility mining is gaining attention towards researchers, as it discovers semantic significance among the items in a database. Utility association rule mining is one of utility mining techniques that retrieves highly profitable and highly associated products in a database. Many researchers started to replace traditional association rule mining with

[1] R. Agrawal , T. Imielinski , A. Swami , Mining association rules between sets of items in large databases, in: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, 1993, pp. 207–216 .
[2] R. Agrawal , R. Srikant , Fast algorithms for mining association rules, in: Proceedings of International Conference on Very Large Data Bases, VLDB’94, 1994, pp. 4 87–4 99 .
[3] Yao, H., Hamilton, H. J., & Geng, L. (2006). A unified framework for utility-based measures for mining itemsets. In Proceedings of ACM SIGKDD 2nd workshop on utility-based data mining (pp. 28–37). ACM.
[4] Chen, Y., Zhao, Y., & Yao, Y. (2007). A profit-based business model for evaluating rule interestingness. In Advances in artificial intelligence. Lecture notes in computer science (Vol. 4509, pp. 296–307). Berlin Heidelberg: Springer.
[5] Chan, R., Yang, Q., & Shen, Y.-D. (2003). Mining high utility itemsets. In Third IEEE international conference on data mining (ICDM 2003) (pp. 19–26). IEEE
[6] J. Sahoo , A.K. Das , A. Goswami , An efficient approach for mining association rules from high utility itemsets, Expert Syst. Appl. 42 (13) (2015) 5754–5778 .
[7] Mai, Thang, Bay Vo, and Loan TT Nguyen, A lattice-based approach for mining high utility association rules, Information Sciences 399 (2017): 81-97.
[8] Lee, D., Park, S. H., & Moon, S. (2013). Utility-based association rule mining: A marketing solution for cross-selling. Expert Systems with applications, 40(7), 2715-2725.
[9] Y. Liu , W. Liao , A. Choudhary ,A Two-Phase algorithm for fast discovery of high utility itemsets, in: Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining, 2005, pp. 689–695 .
[10] V.S. Tseng , C. Wu , B. Shie , P.S. Yu , UP-Growth: an efficient algorithm for high utility itemset mining, in: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2010, pp. 253–262 .
[11] Sivamathi, C., and S. Vijayarani. Performance analysis of utility mining algorithms. Inventive Computation Technologies (ICICT), International Conference on. Vol. 3. IEEE, 2016.
[12] Vijayarani,S., Sivamathi. C , Jeevika Tharani .V. Recent Trends in Utility Mining: A Survey. International Journal of Research in Information Technology.
[13] KannimuthuP, S., PremalathaP, K., & UshaP, G. Survey of recent developments in utility based Data mining.
[14] U. Yun , H. Ryang , K.H. Ryu ,High utility itemset mining with techniques for reducing overestimated utilities and pruning candidates, Expert Syst. Appl. 41 (8) (2014) 3861–3878 .
[15] M.J. Zaki , C.J. Hsiao , Efficient algorithms for mining closed itemsets and their lattice structure, IEEE Trans. Knowl. Data Eng. 17 (4) (2005) 462–478 .
[16] S. Zida, P. Fournier-Viger, J.W. Lin, C. Wu, V.S. Tseng, EFIM: a fast and memory efficient algorithm for high-utility itemset mining, Knowl. Inf. Syst. (2016) 1–31, doi: 10.1007/s10115- 016- 0986-0.
[17] Pramod Pardeshi, Ujwala Patil, "Fuzzy Association Rule Mining- A Survey", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.6, pp.13-18, 2017